Optical–SAR Fusion for Flood Mapping and Post-Event Damage Assessment: A Review of Methods, Applications, and Future Perspectives

Disaster and battle damage scenarios involve abrupt changes, complex environments, and rapidly changing targets, challenging the reliable perception of floods and damaged infrastructure. Optical–SAR fusion exploits complementary optical and synthetic aperture radar (SAR) information and has become an important approach for disaster monitoring and assessment. However, existing fusion methods remain limited by cross-modal registration errors, domain-specific data scarcity, inconsistent validation, and insufficient cross-region and cross-event generalization. This review jointly examines optical–SAR fusion for flood mapping and post-event damage assessment, providing a task-oriented synthesis of conventional fusion, machine learning, deep learning, transformers, self-supervised learning, and generative models. The review synthesizes fusion strategies across sensing conditions, datasets, downstream tasks, and evaluation practices, with particular attention to the applicability and limitations. The synthesis indicates that advanced fusion methods can improve multimodal representation and task-level performance, but increased model complexity does not necessarily ensure robust cross-region or cross-event generalization. Furthermore, the interchangeability between flood mapping and post-event damage evaluation is contingent upon variations in target attributes. It is selected for its supplementary perspectives on the extraction of information related to environmental disasters. Key challenges remain in registration, dataset availability, quantitative assessment, uncertainty, and computational efficiency. Future research should emphasize multimodal foundation models, physics-guided and uncertainty-aware learning, and integrated space–air–ground observation.